The capability of assessing a similarity between images is a basic but complex task that can serve as a building block for a wide range of applications. The applications can comprise e.g. visual search, object detection, object recognition, camera tracking, object tracking, and scene reconstruction.
An image similarity assessment is easily solved by a human, but is a difficult problem from a machine point of view, since it is based on an automatic interpretation of the image content starting from various low-level attributes. Therefore, image matching techniques are employed.
Methods for image matching rely on so called local features. A local feature is a compact description of a patch surrounding a point in an image. The points upon which local features are determined identify characteristic features of the image, e.g. corners, whose detection is stable to illumination, scale, rotation, and perspective changes. Such points are also called keypoints and the similarity between image pairs can be assessed through the number and the positions of keypoints shared by the images. Due to noise effects in the keypoint detection or to mismatching of the keypoints, the matching result typically comprises correct associations, i.e. inliers, and incorrect associations, i.e. outliers.
In Lepsoy, S., Francini, G., Cordara, G., de Gusmao, P. P. B., “Statistical modelling of outliers for fast visual search”, IEEE International Conference on Multimedia and Expo, 11-15 Jul. 2011, an image comparison approach is described.